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检索条件"机构=Data Science & Computer Science Worcester Polytechnic Institute"

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Sequentia12D: Organizing Center of Skip Connections for Transformers 22

Sequentia12D: Organizing Center of Skip Connections for Tran...

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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Pathak, Harsh Nilesh Paffenroth, Randy Hershey, Quincy Data Science Worcester Polytechnic Institute Worcester United States Mathematical Sciences Computer Science and Data Science Worcester Polytechnic Institute Worcester United States

ISBN: (纸本)9798350345346

Skip connections (SCs) are commonly employed in neural networks to facilitate gradient-based training and often lead to improved performance in deep learning. To implement SCs, a user writes custom modules along with *** which limits the extensibility of neural network design with SC. Despite their versatility and user-friendliness, there is an opportunity to create a standardized approach for organizing SCs in various applications. Establishing a clear organizing framework would significantly enhance the widespread adoption of this powerful technique, amplifying its impact. We introduce Sequential2D as an enhanced orchestrator which provides in-sights into not only the width and depth of the network but also a neural network's intra-connections such as SCs. The functionality provided by the Sequential2D architecture is a strict super-set of the functionality found in the common *** network. The 2D in Sequential2D refers to the two-dimensional matrix of functions, as opposed to the one-dimensional cascading array of functions in a Pytorch or Keras sequential container. As an example of our proposed approach, we demonstrate the effectiveness of our Sequential2D architecture by substantially improving the fine-tuning performance of the GPT-2 model with less than 1% additional parameters. © 2023 IEEE.

关键词： deep learning GPT-2 Neural Network nlp recurrent neural networks RNN Sequential skip-connection sparsity transformer

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Exploring Neural Network Structure through Sparse Recurrent Neural Networks: A Recasting and Distillation of Neural Network Hyperparameters 22

Exploring Neural Network Structure through Sparse Recurrent ...

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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Hershey, Quincy Paffenroth, Randy Pathak, Harsh Data Science Worcester Polytechnic Institute Worcester United States Mathematical Sciences Computer Science and Data Science Worcester Polytechnic Institute Worcester United States

ISBN: (纸本)9798350345346

This paper explores the role of sparse parameteri-zations on Recurrent Neural Network (RNN) performance using anomaly detection tasks. The findings indicate sparsity plays a significant role in both improving training stability and overall performance of RNNs. Sparsity of the weight matrix is initially left to vary while the dimensions of the weight matrix remain fixed, causing total trainable weights to vary in response. The sparse RNN models demonstrate surprisingly low performance sensitivity to the resulting changes in total trainable weights. Building on these results, sparsity of the weight matrix is again left to vary with a fixed number of trainable weights and the dimensions of the weight matrix permitted to change with sparsity. By isolating the impact of sparse parameterizations, RNNs show improved training stability and overall performance. Moreover, the results indicate an optimal band of sparsity within which RNNs perform most efficiently across a difficult time-series task. Sparse RNN configurations are compared to LSTM models in anomaly detection tasks. Sparse RNN models demonstrate a strong performance advantage over LSTM models, even out-performing in scenarios where memory is crucial and LSTM models are expected to perform better. The results indicate the reputation of RNN training instability may be exacerbated by overparamaterization resulting from dense configurations. Several characteristics such as model depth and model sparsity are presented which significantly improve the stability and expressiveness of model performance across a wider array of hyperparameters while improving performance differentials at significantly reduced weight counts. Ultimately the paper finds the potential of RNNs to be better realized through sparse parameterizations. © 2023 IEEE.

关键词： recurrent neural networks RNN sparsity

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DeepScreen: Boosting Depression Screening Performance with an Auxiliary Task

DeepScreen: Boosting Depression Screening Performance with a...

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2023 IEEE International Conference on Big data, Bigdata 2023

作者： Flores, Ricardo Shrestha, Avantika Rundensteiner, Elke A. Worcester Polytechnic Institute Data Science WorcesterMA United States Worcester Polytechnic Institute Computer Science/Data Science WorcesterMA United States

ISBN: (纸本)9798350324457

Depression is a prevalent mental health condition with severe impacts on physical and social health. It is costly and difficult to detect, requiring substantial time from trained mental professionals. To alleviate this burden, recent research explores the diagnostic capabilities of deep learning models trained on modalities extracted from videos of clinical interviews for depression screening. However, training deep learning models is challenging because in the mental health domain datasets contain a small number of patients. To address this challenge, we propose DeepScreen, a recurrent deep-learning architecture for depression screening whose performance is boosted by deploying a self-supervised auxiliary task for selective missing value imputation. DeepScreen leverages a multi-task architecture with a bidirectional recurrent deep learning model and a self-attention mechanism, jointly optimizing the supervised depression and the self-supervised auxiliary task. Our first study assesses the capability of the auxiliary task training of DeepScreen under different correlation levels and masking sub-sequence sizes of multi-variate time series. Found to be effective, our second study evaluates DeepScreen on 15 data sets composed of real-world temporal facial landmark features extracted from responses to different clinical interview questions. The results achieved across all 15 datasets demonstrate that the imputation task boosts the depression prediction metrics significantly. Specifically, DeepScreen improves the F1 score for one of the datasets by 57%. Further, our best-performing model achieves an F1 score of 0.85. This work provides valuable insights into improving deep learning driven mental screening applications by leveraging auxiliary tasks such as imputation for learning a better representation even from small datasets. © 2023 IEEE.

关键词： Digital health missing value imputation multivariate time series sequential modeling for mental health screen

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Leveraging LLMs for Integrated Sentiment and Topic Analysis on African Social Media 23

Leveraging LLMs for Integrated Sentiment and Topic Analysis ...

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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Sibitenda, Harriet Hu, Ruofan Rundensteiner, Elke Diattara, Awa Traore, Assitan Ba, Cheikh University of Gaston Berger Computer Science Department Saint Louis Senegal Data Science Program Worcester Polytechnic Institute Worcester United States Data Science Program Worcester Polytechnic Institute Worcester United States Actroll Data Science Department France

ISBN: (纸本)9798350374889

Leveraging AI to analyze key topics on African social media can enhance public governance. Our study analyzes social media discourse within African society on development concerns by (1) evaluating AI techniques for sentiment, topic, and theme extraction, comparing the accuracy of these methods with human annotations, and (2) extracting key insights from the data to provide policymakers with actionable recommendations for sustainable development. For this study, we utilized a data corpus of 22,036 posts from Twitter and YouTube, all focused on development issues in Africa. We applied topic modeling to extract relevant topics from the corpus and used similarity analysis, powered by Large Language Models, to link these topics to prevalent development themes. Additionally, we leveraged unsupervised models such as VADER and Large Language Models to extract sentiment related to the identified topics. To validate these model-generated sentiments, we conducted a small crowdsourced study to gather human-annotated labels as ground truth. Our sentiment analysis findings show improvements with models like TextBlob, VADER, and Llama. Fine-tuning, partic-ularly with BERT, achieved an impressive Fl score of 0.988. Meanwhile, Llama demonstrated strong precision (0.72) and balanced accuracy (0.55) in capturing contextual sentiment. We identified 304 topics using BERTopic and Llama, with robust coherence (0.81 C-v) and divergence (0.58 IRBO). In theme analysis, the One-vs-Rest classification with ensemble voting performed exceptionally well, with 'Poverty' achieving the highest F1 score of 0.89. Our results suggest that African policymakers prioritize addressing corruption, unemployment, drought, and instability, while closely monitoring the positive impacts of policy interventions. © 2024 IEEE.

关键词： Tweets

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One-class Classification Using Autoencoder Feature Residuals for Improved IoT Network Intrusion Detection 32

One-class Classification Using Autoencoder Feature Residuals...

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32nd International Conference on computer Communications and Networks, ICCCN 2023

作者： Lewandowski, Brian Paffenroth, Randy Worcester Polytechnic Institute Computer Science Worcester United States Worcester Polytechnic Institute Data Science Mathematical Sciences and Computer Science Worcester United States

ISBN: (纸本)9798350336184

Networks are rapidly evolving to include more Internet of Things devices as we grow to rely on them for smart home services, health infrastructure, and industrial development. Along with this proliferation, these networks are often the target of cyberattacks that seek to take advantage of their low processing power and the complexity that is involved in protecting heterogeneous networks. We seek to improve our network intrusion detection capabilities for Internet of Things networks by developing methods of detection that utilize the power of deep learning and have the ability to detect zero-day attacks. In this work, we outline a novel feature generation process using autoencoder feature residuals that can be combined with one-class classifiers to effectively detect network attacks on Internet of Things networks using no attack data during the training process. Moreover, we show that our novel feature sets are able to outperform using an original feature set leading to a reduction of typical model hyperparameter tuning activities. © 2023 IEEE.

关键词： Internet of things

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Conditioned Cycles in Sparse data Domains: Applications to Electromagnetics 22

Conditioned Cycles in Sparse Data Domains: Applications to E...

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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Barger, Maria Paffenroth, Randy Pathak, Harsh Worcester Polytechnic Institute Data Science WorcesterMA United States Worcester Polytechnic Institute Mathematics Computer Science and Data Science WorcesterMA United States

ISBN: (纸本)9798350345346

Training neural networks is difficult with small datasets, yet small training data sets are commonly found with machine learning problems in the physical sciences. Prediction accuracy within such sparse data domains can be improved by the utilization of conditioning, inspired by classical statistics, and cycles, similar to those of CycleGANs. In this paper, we propose the addition of 'oracle' conditioning, where the true condition value is provided during training (but not during testing), to improve final prediction accuracy in sparse data domains. For example, in over-water electromagnetic (EM) source localization, the range of the emitter can be thought of as a 'condition' on the generation of a received EM signal. Oracle conditioning utilizes an encoder-decoder framework where the conditioning of the decoder is not contingent on the output of an encoder's prediction (e.g, predicting the range from a received EM signal). While our primary application involves source localization for EM signals, this paper is intended to inspire a general approach toward neural network training for problems in the physical sciences with similar characteristics. Our multi-component loss function is intended to serve as a general method for small data problems which can benefit from the addition of cycles and oracle conditioning. © 2023 IEEE.

关键词： conditional generation cycles electromagnetics loss function oracle conditioning

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An Integrated Framework for Team Formation and Winner Prediction in the FIRST Robotics Competition: Model, Algorithm, and Analysis 25

An Integrated Framework for Team Formation and Winner Predic...

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25th IEEE International Conferences on High Performance Computing and Communications, 9th International Conference on data science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Galbiati, Federico Gran, Ranier X. Jacques, Brendan D. Mulhern, Sullivan J. Ngan, Chun-Kit Worcester Polytechnic Institute Department of Computer Science Worcester United States Worcester Polytechnic Institute Data Science Program Worcester United States

ISBN: (纸本)9798350330014

This research work aims to develop an analytical approach for optimizing team formation and predicting team performance in a competitive environment based on data on the competitors' skills prior to the team formation. There are several approaches in scientific literature to optimize and predict a team's performance. However, most studies employ fine-grained skill statistics of the individual members or constraints such as teams with a set group of members. Currently, no research tackles the highly constrained domain of the FIRST Robotics Competition. This research effort aims to fill this gap by providing an analytical method for optimizing and predicting team performance in a competitive environment while allowing these constraints and only using metrics on previous team performance, not on each individual member's performance. We apply our method to the drafting process of the FIRST Robotics competition, a domain in which the skills change year-over-year, team members change throughout the season, each match only has a superficial set of statistics, and alliance formation is key to competitive success. First, we develop a method that could extrapolate individual members' performance based on overall team performance. An alliance optimization algorithm is developed to optimize team formation and a deep neural network model is trained to predict the winning team, both using highly post-processed real-world data. Our method is able to successfully extract individual members' metrics from overall team statistics, form competitive teams, and predict the winning team with 84.08 % accuracy. © 2023 IEEE.

关键词： Predictive analytics

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DeepSC-Edge: Scientific Corrosion Segmentation with Edge-Guided and Class-Balanced Losses 22

DeepSC-Edge: Scientific Corrosion Segmentation with Edge-Gui...

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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Yin, Biao Josselyn, Nicholas Considine, Thomas Kelley, John Rinderspacher, Berend Jensen, Robert Snyder, James Zhang, Ziming Rundensteiner, Elke Worcester Polytechnic Institute Department of Computer Science WorcesterMA United States Worcester Polytechnic Institute Department of Data Science WorcesterMA United States Worcester Polytechnic Institute Department of Electrical and Computer Engineering WorcesterMA United States

ISBN: (纸本)9798350345346

Corrosion is a prevalent issue in numerous industrial fields, causing expenses nearing 3 trillion or 4% of the GDP annually with safety threats and environmental pollution. To timely qualify and validate new corrosion-inhibiting materials on a large scale, accurate and efficient corrosion assessment is crucial. Yet it is hindered by a lack of automatic tools for expert-level corrosion segmentation of material science experimental images. Developing such tools is challenging due to limited domain-valid data, image artifacts visually similar to corrosion, various corrosion morphology, strong class imbalance, and millimeter-precision corrosion boundaries. To help the community address these challenges, we curate the first expert-level segmentation annotations for a real-world image dataset [1] for scientific corrosion segmentation. In addition, we design a deep learning based model, called DeepSC-Edge that achieves guidance of ground-truth edge learning by adopting a novel loss that avoids over-fitting to edges. It also is enriched by integrating a class-balanced loss that improves segmentation with small area but crucial edges of interest for scientific corrosion assessment. Our dataset and methods pave the way to advanced deep-learning models for corrosion assessment and generation - promoting new research to connect computer vision and material science discovery. Once the appropriate approvals have been cleared, we expect to release the code and data at: https://***/ © 2023 IEEE.

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Contrastive Learning with Auxiliary User Detection for Identifying Activities 23

Contrastive Learning with Auxiliary User Detection for Ident...

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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Ge, Wen Mou, Guanyi Agu, Emmanuel O. Lee, Kyumin Worcester Polytechnic Institute Computer Science Department WorcesterMA United States

ISBN: (纸本)9798350374889

Human Activity Recognition (HAR) is an important task in ubiquitous computing, with impactful real-world applications. While recent state-of-the-art HAR research has demonstrated impressive performance, some key aspects remain under-explored. First, we believe that for optimal performance, HAR models should be both Context-Aware (CA) and personalized. However, prior work has predominantly focused on being Context-Aware (CA), largely ignoring being User-Aware (UA). We argue that learning user-specific differences in performing various activities is as critical as considering user context while performing HAR tasks. Secondly, we believe that the predictions of HAR models should be unified, reliably recognizing the same activity even when performed by different users. As such, the representations utilized by CA and UA models should explicitly place different users performing the same activity closer together. Moreover, identifying the user performing an activity is useful in applications such as thwarting cheating by having another person perform medically-prescribed activities. To bridge this gap, we introduce Contrastive Learning with Auxiliary User Detection for Identifying Activities (CLAUDIA), a novel framework designed to address these issues. Specifically, we expand the contextual scope of the CAHAR task by integrating User Identification (UI) within the CAHAR framework, jointly predicting both CAHAR and UI in a new task called User and Context-Aware HAR (UCA-HAR). This approach enriches personalized and contextual understanding by jointly learning user-invariant and user-specific patterns. Inspired by state-of-the-art designs in the visual domain, we introduce a supervised contrastive loss objective on instance-instance pairs to enhance model efficacy and improve learned feature quality. Through theoretical exposition, empirical analysis of real-world datasets, and rigorous experimentation, we demonstrate the significance of each component of CLAUDIA and discus

关键词： Contrastive Learning

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Multi-task Learning with Pre-trained Language Models for Mental Illness Screening

Multi-task Learning with Pre-trained Language Models for Men...

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Shrestha, Avantika Tlachac, M.L. Flores, Ricardo Hickey, Kevin Rundensteiner, Elke A. Worcester Polytechnic Institute Data Science Department WorcesterMA United States Bryant University Center for Health and Behavioral Sciences Department of Information Systems and Analytics SmithfieldRI United States University of Concepcion Department of Computer Science Biobio Concepcion Chile Worcester Polytechnic Institute Data Science Department Computer Science Department WorcesterMA United States

ISBN: (纸本)9798350362480

Major depressive disorder (MDD) and post-traumatic stress disorder (PTSD) are mental disorders that reduce quality of life. As they are challenging to detect in a timely manner, recent studies explore the mental illness screening potential of language models on modalities such as transcripts. Such datasets suffer from a limited number of participants. To overcome these challenges, we take a two-pronged approach: (1) we leverage multi-task learning to model multiple illnesses concurrently namely MDD and PTSD screening, and (2) we plug in pre-trained language models as a backbone, namely, Bidirectional Encoder Representations from Transformers (BERT) variants - which are capable of learning the linguistic content of clinical interview transcripts. In particular, we experiment with three multi-task weighting strategies and five BERT variants, applying them to 15 transcript sets extracted from the Distress Analysis Interview Corpus. Our results indicate that leveraging multi-task learning, especially with meta-weighting, increases the screening performance compared to single-task learning. Our multi-task learning model strategies improved the F1 scores for all 15 datasets for both MDD and PTSD screening. Notably, multitask learning improved MDD screening ability by 20 percent with transcripts regarding 'regret' and achieved F1 of 0.89 and 0.82 respectively for MDD and PTSD screening with transcripts regarding 'medical history'. Our findings may help researchers develop more effective mental illness screening models. © 2024 IEEE.

关键词： Multi-task learning

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